{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "55cb3e63-e778-4291-8817-0f49b2adfb4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# file name: emg_main\n",
    "# author: ultralyj\n",
    "# e-mail: 1951578@tongji.edu.cn\n",
    "# version: v1.0\n",
    "# date: 2022-12-5\n",
    "# brief: main body\n",
    "\n",
    "import h5py\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "from torchsummary import summary\n",
    "import torchvision.models as models\n",
    "import torchvision.transforms as tt\n",
    "import torch.nn.functional as F\n",
    "import time\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a6a9f89",
   "metadata": {},
   "source": [
    "## 1. 数据可视化\n",
    "数据集字典\n",
    "# dict_keys(['__header__', '__version__', '__globals__', \n",
    "# 'subject', 'exercise', 'emg', 'acc', 'gyro', 'mag', \n",
    "# 'stimulus', 'glove', 'repetition', 'restimulus', 'rerepetition'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53de62a3",
   "metadata": {},
   "source": [
    "### 1.1 原始数据波形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e2a955db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load file: Subject_1/S1_E1_A1.mat...[ok]:1797052\n"
     ]
    }
   ],
   "source": [
    "from ninapro_utils import *\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "s1e1_dict = get_ninapro('E:/db7/','Subject_1/S1_E1_A1.mat')\n",
    "colors=list(mcolors.TABLEAU_COLORS.keys()) #颜色变化\n",
    "\n",
    "plt.plot(s1e1_dict['emg'][416000:423000,0]*1e3,linewidth=0.3,color=mcolors.TABLEAU_COLORS[colors[int(math.fabs(1))]])\n",
    "plt.plot(s1e1_dict['emg'][416000:423000,0]*1e3+1,linewidth=0.3,color=mcolors.TABLEAU_COLORS[colors[int(math.fabs(2))]])\n",
    "plt.plot(s1e1_dict['emg'][416000:423000,0]*1e3+2,linewidth=0.3,color=mcolors.TABLEAU_COLORS[colors[int(math.fabs(3))]])\n",
    "plt.plot(s1e1_dict['emg'][416000:423000,0]*1e3+3,linewidth=0.3,color=mcolors.TABLEAU_COLORS[colors[int(math.fabs(4))]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e6dbe256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ab435c7520>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# # 未校正的刺激label\n",
    "# plt.plot(s1e1_dict['stimulus'][400000:500000])\n",
    "# # 校正后的刺激label\n",
    "# plt.plot(s1e1_dict['restimulus'][400000:500000],linewidth= 0.4)\n",
    "colors=list(mcolors.TABLEAU_COLORS.keys()) #颜色变化\n",
    "\n",
    "plt.plot(s1e1_dict['emg'][416000:423000,0]*1e3,linewidth=0.3,color=mcolors.TABLEAU_COLORS[colors[int(math.fabs(1))]])\n",
    "plt.plot(s1e1_dict['emg'][416000:423000,0]*1e3+1,linewidth=0.3,color=mcolors.TABLEAU_COLORS[colors[int(math.fabs(2))]])\n",
    "plt.plot(s1e1_dict['emg'][416000:423000,0]*1e3+2,linewidth=0.3,color=mcolors.TABLEAU_COLORS[colors[int(math.fabs(3))]])\n",
    "plt.plot(s1e1_dict['emg'][416000:423000,0]*1e3+3,linewidth=0.3,color=mcolors.TABLEAU_COLORS[colors[int(math.fabs(4))]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f200e8",
   "metadata": {},
   "source": [
    "### 1.2 缓存数据波形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c869c8d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "open h5 file:../data/s1_f.h5...[ok]data: (4327442, 12) , label:(4327442, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b3eed9f520>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d,l = read_data('../data/s1_f.h5')\n",
    "plt.plot(d[400000:500000,:]*1e3)\n",
    "plt.plot(l[400000:500000]+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "473c210e",
   "metadata": {},
   "source": [
    "### 1.3 切片后的数据波形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e3ff035b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "open h5 file:../data/ss1_f.h5...[ok]data: (8066, 256, 12) , label:(8066, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b3eeeec760>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d,l = read_data('../data/ss1_f.h5') # 去除了0信号\n",
    "l = np.tile(l,256)\n",
    "l = np.ravel(l)\n",
    "d = np.reshape(d,(8066*256,12))\n",
    "plt.plot(d[150000:250000,:]*1e3)\n",
    "plt.plot(l[150000:250000]+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be4c6d9a-72cd-435f-938c-58a948ca6e3b",
   "metadata": {},
   "source": [
    "# 网络结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3da13a1e-135e-405b-a24f-7d05729e2d4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model will be running on cuda:0 device\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1           [-1, 64, 6, 128]           3,136\n",
      "       BatchNorm2d-2           [-1, 64, 6, 128]             128\n",
      "              ReLU-3           [-1, 64, 6, 128]               0\n",
      "         MaxPool2d-4            [-1, 64, 3, 64]               0\n",
      "            Conv2d-5            [-1, 64, 3, 64]          36,864\n",
      "       BatchNorm2d-6            [-1, 64, 3, 64]             128\n",
      "              ReLU-7            [-1, 64, 3, 64]               0\n",
      "            Conv2d-8            [-1, 64, 3, 64]          36,864\n",
      "       BatchNorm2d-9            [-1, 64, 3, 64]             128\n",
      "             ReLU-10            [-1, 64, 3, 64]               0\n",
      "       BasicBlock-11            [-1, 64, 3, 64]               0\n",
      "           Conv2d-12            [-1, 64, 3, 64]          36,864\n",
      "      BatchNorm2d-13            [-1, 64, 3, 64]             128\n",
      "             ReLU-14            [-1, 64, 3, 64]               0\n",
      "           Conv2d-15            [-1, 64, 3, 64]          36,864\n",
      "      BatchNorm2d-16            [-1, 64, 3, 64]             128\n",
      "             ReLU-17            [-1, 64, 3, 64]               0\n",
      "       BasicBlock-18            [-1, 64, 3, 64]               0\n",
      "           Conv2d-19           [-1, 128, 2, 32]          73,728\n",
      "      BatchNorm2d-20           [-1, 128, 2, 32]             256\n",
      "             ReLU-21           [-1, 128, 2, 32]               0\n",
      "           Conv2d-22           [-1, 128, 2, 32]         147,456\n",
      "      BatchNorm2d-23           [-1, 128, 2, 32]             256\n",
      "           Conv2d-24           [-1, 128, 2, 32]           8,192\n",
      "      BatchNorm2d-25           [-1, 128, 2, 32]             256\n",
      "             ReLU-26           [-1, 128, 2, 32]               0\n",
      "       BasicBlock-27           [-1, 128, 2, 32]               0\n",
      "           Conv2d-28           [-1, 128, 2, 32]         147,456\n",
      "      BatchNorm2d-29           [-1, 128, 2, 32]             256\n",
      "             ReLU-30           [-1, 128, 2, 32]               0\n",
      "           Conv2d-31           [-1, 128, 2, 32]         147,456\n",
      "      BatchNorm2d-32           [-1, 128, 2, 32]             256\n",
      "             ReLU-33           [-1, 128, 2, 32]               0\n",
      "       BasicBlock-34           [-1, 128, 2, 32]               0\n",
      "           Conv2d-35           [-1, 256, 1, 16]         294,912\n",
      "      BatchNorm2d-36           [-1, 256, 1, 16]             512\n",
      "             ReLU-37           [-1, 256, 1, 16]               0\n",
      "           Conv2d-38           [-1, 256, 1, 16]         589,824\n",
      "      BatchNorm2d-39           [-1, 256, 1, 16]             512\n",
      "           Conv2d-40           [-1, 256, 1, 16]          32,768\n",
      "      BatchNorm2d-41           [-1, 256, 1, 16]             512\n",
      "             ReLU-42           [-1, 256, 1, 16]               0\n",
      "       BasicBlock-43           [-1, 256, 1, 16]               0\n",
      "           Conv2d-44           [-1, 256, 1, 16]         589,824\n",
      "      BatchNorm2d-45           [-1, 256, 1, 16]             512\n",
      "             ReLU-46           [-1, 256, 1, 16]               0\n",
      "           Conv2d-47           [-1, 256, 1, 16]         589,824\n",
      "      BatchNorm2d-48           [-1, 256, 1, 16]             512\n",
      "             ReLU-49           [-1, 256, 1, 16]               0\n",
      "       BasicBlock-50           [-1, 256, 1, 16]               0\n",
      "           Conv2d-51            [-1, 512, 1, 8]       1,179,648\n",
      "      BatchNorm2d-52            [-1, 512, 1, 8]           1,024\n",
      "             ReLU-53            [-1, 512, 1, 8]               0\n",
      "           Conv2d-54            [-1, 512, 1, 8]       2,359,296\n",
      "      BatchNorm2d-55            [-1, 512, 1, 8]           1,024\n",
      "           Conv2d-56            [-1, 512, 1, 8]         131,072\n",
      "      BatchNorm2d-57            [-1, 512, 1, 8]           1,024\n",
      "             ReLU-58            [-1, 512, 1, 8]               0\n",
      "       BasicBlock-59            [-1, 512, 1, 8]               0\n",
      "           Conv2d-60            [-1, 512, 1, 8]       2,359,296\n",
      "      BatchNorm2d-61            [-1, 512, 1, 8]           1,024\n",
      "             ReLU-62            [-1, 512, 1, 8]               0\n",
      "           Conv2d-63            [-1, 512, 1, 8]       2,359,296\n",
      "      BatchNorm2d-64            [-1, 512, 1, 8]           1,024\n",
      "             ReLU-65            [-1, 512, 1, 8]               0\n",
      "       BasicBlock-66            [-1, 512, 1, 8]               0\n",
      "AdaptiveAvgPool2d-67            [-1, 512, 1, 1]               0\n",
      "           Linear-68                   [-1, 40]          20,520\n",
      "================================================================\n",
      "Total params: 11,190,760\n",
      "Trainable params: 11,190,760\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.01\n",
      "Forward/backward pass size (MB): 4.54\n",
      "Params size (MB): 42.69\n",
      "Estimated Total Size (MB): 47.24\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchvision import models\n",
    "\n",
    "\n",
    "class GengNet(nn.Module):\n",
    "    def __init__(self, class_num=None, base_features=64, window_length=256, input_channels=6):\n",
    "        super(GengNet, self).__init__()\n",
    "        self.class_num = class_num\n",
    "        self.conv1 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=1,  # for EMG images, the channels is 1. not the signal channels: input_channels\n",
    "                      out_channels=base_features,\n",
    "                      kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm2d(base_features),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=base_features,\n",
    "                      out_channels=base_features,\n",
    "                      kernel_size=3, stride=1, padding=1),\n",
    "            nn.BatchNorm2d(base_features),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        self.conv3 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=base_features,\n",
    "                      out_channels=base_features,\n",
    "                      kernel_size=1, stride=1),\n",
    "            nn.BatchNorm2d(base_features),\n",
    "            nn.ReLU(),\n",
    "        )\n",
    "        self.conv4 = nn.Sequential(\n",
    "            nn.Conv2d(in_channels=base_features,\n",
    "                      out_channels=base_features,\n",
    "                      kernel_size=1, stride=1),\n",
    "            nn.BatchNorm2d(base_features),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5),\n",
    "        )\n",
    "\n",
    "        self.fcn1 = nn.Sequential(\n",
    "            nn.Linear(base_features * window_length * input_channels, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.Linear(512, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5)\n",
    "        )\n",
    "        self.fcn2 = nn.Linear(128, self.class_num)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "\n",
    "        x = self.fcn1(x.view(x.size(0), -1))\n",
    "        x = self.fcn2(x)\n",
    "        x = F.softmax(x, dim=1)\n",
    "        return x\n",
    "        \n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(\"The model will be running on\", device, \"device\")\n",
    "model_test = models.resnet18(num_classes=40)\n",
    "model_test.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
    "model_test = model_test.to(device)\n",
    "summary(model_test,(1,12,256))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "258f5483-be6c-4106-ace4-8d40f585ec85",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87675eba-e88d-4007-80e0-dd32f8d303bd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "af7a5773-74c0-4170-bbc1-3858f9f4aa11",
   "metadata": {},
   "source": [
    "# 数据训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6fee85c6-4be5-49dd-ab01-0c475dcb358e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def testAccuracy(device, model, test_loader):\n",
    "    \n",
    "    model.eval()\n",
    "    accuracy = 0.0\n",
    "    total = 0.0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for data in test_loader:\n",
    "            emg_data, labels = data\n",
    "            emg_data = Variable(emg_data.to(device))    # torch.Size([64, 1, 200, 12])\n",
    "            labels = Variable(labels.to(device))        # torch.Size([64])\n",
    "            # run the model on the test set to predict labels\n",
    "            outputs = model(emg_data)\n",
    "            # the label with the highest energy will be our prediction\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            accuracy += (predicted == labels).sum().item()\n",
    "    \n",
    "    # compute the accuracy over all test images\n",
    "    accuracy = (100 * accuracy / total)\n",
    "    return(accuracy)\n",
    "\n",
    "def train(device, num_epochs, train_loader, test_loader):\n",
    "    best_accuracy = 0\n",
    "    model = GengNet(class_num=40,base_features=16,window_length=256,input_channels=12).to(device)\n",
    "    loss_func = torch.nn.CrossEntropyLoss()\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "\n",
    "    \n",
    "    train_accs = []\n",
    "    train_loss = []\n",
    "    test_accs = []\n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        running_loss = 0.0\n",
    "        for i,(inputs, labels) in enumerate(train_loader,0):#0是下标起始位置默认为0\n",
    "            # data 的格式[[inputs, labels]]       \n",
    "    #         inputs,labels = data\n",
    "            inputs = Variable(inputs.to(device))    # torch.Size([64, 1, 200, 12])\n",
    "            labels = Variable(labels.to(device))        # torch.Size([64]) \n",
    "            #初始为0，清除上个batch的梯度信息\n",
    "            optimizer.zero_grad()         \n",
    "\n",
    "            #前向+后向+优化     \n",
    "            outputs = model(inputs)\n",
    "            loss = loss_func(outputs,labels.long())\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            # loss 的输出，每个一百个batch输出，平均的loss\n",
    "            running_loss += loss.item()\n",
    "            if i%100 == 99:\n",
    "                print('[%d,%5d] loss :%.3f' %\n",
    "                    (epoch+1,i+1,running_loss/100),end='',flush=True)\n",
    "                running_loss = 0.0\n",
    "            train_loss.append(loss.item())\n",
    "\n",
    "            # 训练曲线的绘制 一个batch中的准确率\n",
    "            correct = 0\n",
    "            total = 0\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total = labels.size(0)# labels 的长度\n",
    "            correct = (predicted == labels).sum().item() # 预测正确的数目\n",
    "            train_accs.append(100*correct/total)\n",
    "            if i%100 == 99:\n",
    "                print(' acc=%d'%(100*correct/total))\n",
    "            \n",
    "        accuracy = testAccuracy(device, model, test_loader)\n",
    "        print('For epoch', epoch+1,'the test accuracy over the whole test set is %d %%' % (accuracy))\n",
    "        # we want to save the model if the accuracy is the best\n",
    "        if accuracy > best_accuracy:\n",
    "            best_accuracy = accuracy\n",
    "            torch.save(model.state_dict(), \"../model/best_epoch{1}_{0}.pth\".format(epoch, (int(time.time())%1000000)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "52672b1c-d3f9-413e-ac42-64c7bb52aede",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def merge_emg_data(b,e):\n",
    "    d = None\n",
    "    l = None\n",
    "    for i in range(b, e + 1):\n",
    "        d0,l0 = read_data('../data/ss{0}_f.h5'.format(i))\n",
    "        for j in range(12):\n",
    "            d0[:,:,j] = np.linalg.norm(d0[:,:,j])\n",
    "        if (i==b):\n",
    "            d = d0\n",
    "            l = l0\n",
    "        else:\n",
    "            d = np.concatenate((d,d0),axis=0)\n",
    "            l = np.concatenate((l,l0),axis=0)\n",
    "    return d,l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ef3292d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "open h5 file:../data/ss1_f.h5...[ok]data: (8066, 256, 12) , label:(8066, 1)\n",
      "open h5 file:../data/ss2_f.h5...[ok]data: (8673, 256, 12) , label:(8673, 1)\n",
      "open h5 file:../data/ss3_f.h5...[ok]data: (7144, 256, 12) , label:(7144, 1)\n",
      "open h5 file:../data/ss4_f.h5...[ok]data: (9527, 256, 12) , label:(9527, 1)\n",
      "open h5 file:../data/ss19_f.h5...[ok]data: (6902, 256, 12) , label:(6902, 1)\n",
      "open h5 file:../data/ss20_f.h5...[ok]data: (6778, 256, 12) , label:(6778, 1)\n",
      "[train]:33410, [test]:13680\n",
      "[0.05749    0.07376164 0.02986876 0.01197546 0.02984221 0.07849508\n",
      " 0.04374366 0.03374061 0.03262019 0.04832094 0.04050371 0.01096751]\n"
     ]
    }
   ],
   "source": [
    "data_train,label_train = merge_emg_data(1,4)\n",
    "data_test,label_test = merge_emg_data(19,20)\n",
    "data_train = data_train.astype(np.float32)\n",
    "data_test = data_test.astype(np.float32)\n",
    "\n",
    "print('[train]:%d, [test]:%d'%(len(label_train),len(label_test)))\n",
    "print(data_test[0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0d94a3f2-9978-4e5c-8b12-8b68bec4f7bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape:  (33410, 1, 256, 12) label shape:  (33410,)\n",
      "get dataloader...[ok]\n",
      "[1,  100] loss :3.702 acc=1\n",
      "[1,  200] loss :3.701 acc=4\n",
      "[1,  300] loss :3.702 acc=3\n",
      "[1,  400] loss :3.702 acc=1\n",
      "[1,  500] loss :3.707 acc=6\n",
      "For epoch 1 the test accuracy over the whole test set is 1 %\n",
      "[2,  100] loss :3.706 acc=3\n",
      "[2,  200] loss :3.708 acc=1\n",
      "[2,  300] loss :3.705 acc=4\n",
      "[2,  400] loss :3.706 acc=0\n",
      "[2,  500] loss :3.708 acc=3\n",
      "For epoch 2 the test accuracy over the whole test set is 1 %\n",
      "[3,  100] loss :3.707 acc=7\n",
      "[3,  200] loss :3.707 acc=0\n",
      "[3,  300] loss :3.705 acc=3\n",
      "[3,  400] loss :3.707 acc=3\n",
      "[3,  500] loss :3.706 acc=1\n",
      "For epoch 3 the test accuracy over the whole test set is 1 %\n",
      "[4,  100] loss :3.707 acc=3\n",
      "[4,  200] loss :3.705 acc=0\n",
      "[4,  300] loss :3.706 acc=0\n",
      "[4,  400] loss :3.706 acc=3\n",
      "[4,  500] loss :3.709 acc=3\n",
      "For epoch 4 the test accuracy over the whole test set is 1 %\n",
      "[5,  100] loss :3.704 acc=1\n",
      "[5,  200] loss :3.706 acc=4\n",
      "[5,  300] loss :3.708 acc=1\n",
      "[5,  400] loss :3.708 acc=1\n",
      "[5,  500] loss :3.705 acc=1\n",
      "For epoch 5 the test accuracy over the whole test set is 1 %\n",
      "[6,  100] loss :3.707 acc=1\n",
      "[6,  200] loss :3.710 acc=1\n",
      "[6,  300] loss :3.706 acc=4\n",
      "[6,  400] loss :3.704 acc=7\n",
      "[6,  500] loss :3.705 acc=1\n",
      "For epoch 6 the test accuracy over the whole test set is 1 %\n",
      "[7,  100] loss :3.707 acc=0\n",
      "[7,  200] loss :3.707 acc=1\n",
      "[7,  300] loss :3.707 acc=1\n",
      "[7,  400] loss :3.704 acc=3\n",
      "[7,  500] loss :3.707 acc=3\n",
      "For epoch 7 the test accuracy over the whole test set is 1 %\n",
      "[8,  100] loss :3.706 acc=0\n",
      "[8,  200] loss :3.706 acc=1\n",
      "[8,  300] loss :3.708 acc=0\n",
      "[8,  400] loss :3.704 acc=3\n",
      "[8,  500] loss :3.707 acc=3\n",
      "For epoch 8 the test accuracy over the whole test set is 1 %\n",
      "[9,  100] loss :3.703 acc=1\n",
      "[9,  200] loss :3.706 acc=1\n",
      "[9,  300] loss :3.709 acc=1\n",
      "[9,  400] loss :3.707 acc=1\n",
      "[9,  500] loss :3.706 acc=3\n",
      "For epoch 9 the test accuracy over the whole test set is 1 %\n",
      "[10,  100] loss :3.705 acc=1\n",
      "[10,  200] loss :3.708 acc=1\n",
      "[10,  300] loss :3.708 acc=1\n",
      "[10,  400] loss :3.704 acc=1\n",
      "[10,  500] loss :3.707 acc=3\n",
      "For epoch 10 the test accuracy over the whole test set is 1 %\n",
      "[11,  100] loss :3.706 acc=4\n",
      "[11,  200] loss :3.705 acc=3\n",
      "[11,  300] loss :3.704 acc=1\n",
      "[11,  400] loss :3.707 acc=3\n",
      "[11,  500] loss :3.708 acc=4\n",
      "For epoch 11 the test accuracy over the whole test set is 1 %\n",
      "[12,  100] loss :3.706 acc=1\n",
      "[12,  200] loss :3.708 acc=1\n",
      "[12,  300] loss :3.704 acc=7\n",
      "[12,  400] loss :3.708 acc=0\n",
      "[12,  500] loss :3.706 acc=0\n",
      "For epoch 12 the test accuracy over the whole test set is 1 %\n",
      "[13,  100] loss :3.704 acc=1\n",
      "[13,  200] loss :3.708 acc=3\n",
      "[13,  300] loss :3.706 acc=3\n",
      "[13,  400] loss :3.707 acc=7\n",
      "[13,  500] loss :3.706 acc=1\n",
      "For epoch 13 the test accuracy over the whole test set is 1 %\n",
      "[14,  100] loss :3.705 acc=1\n",
      "[14,  200] loss :3.708 acc=0\n",
      "[14,  300] loss :3.705 acc=3\n",
      "[14,  400] loss :3.706 acc=4\n",
      "[14,  500] loss :3.706 acc=0\n",
      "For epoch 14 the test accuracy over the whole test set is 1 %\n",
      "[15,  100] loss :3.709 acc=3\n",
      "[15,  200] loss :3.706 acc=3\n",
      "[15,  300] loss :3.707 acc=1\n",
      "[15,  400] loss :3.707 acc=1\n",
      "[15,  500] loss :3.702 acc=0\n",
      "For epoch 15 the test accuracy over the whole test set is 1 %\n",
      "[16,  100] loss :3.708 acc=3\n",
      "[16,  200] loss :3.707 acc=4\n",
      "[16,  300] loss :3.705 acc=3\n",
      "[16,  400] loss :3.707 acc=1\n",
      "[16,  500] loss :3.706 acc=3\n",
      "For epoch 16 the test accuracy over the whole test set is 1 %\n",
      "[17,  100] loss :3.708 acc=4\n",
      "[17,  200] loss :3.709 acc=0\n",
      "[17,  300] loss :3.705 acc=3\n",
      "[17,  400] loss :3.707 acc=4\n",
      "[17,  500] loss :3.704 acc=4\n",
      "For epoch 17 the test accuracy over the whole test set is 1 %\n",
      "[18,  100] loss :3.708 acc=1\n",
      "[18,  200] loss :3.702 acc=4\n",
      "[18,  300] loss :3.707 acc=0\n",
      "[18,  400] loss :3.710 acc=1\n",
      "[18,  500] loss :3.705 acc=0\n",
      "For epoch 18 the test accuracy over the whole test set is 1 %\n",
      "[19,  100] loss :3.708 acc=3\n",
      "[19,  200] loss :3.706 acc=1\n",
      "[19,  300] loss :3.706 acc=0\n",
      "[19,  400] loss :3.708 acc=0\n",
      "[19,  500] loss :3.704 acc=1\n",
      "For epoch 19 the test accuracy over the whole test set is 1 %\n",
      "[20,  100] loss :3.706 acc=3\n",
      "[20,  200] loss :3.706 acc=4\n",
      "[20,  300] loss :3.708 acc=0\n",
      "[20,  400] loss :3.704 acc=0\n",
      "[20,  500] loss :3.705 acc=3\n",
      "For epoch 20 the test accuracy over the whole test set is 1 %\n",
      "[21,  100] loss :3.705 acc=7\n",
      "[21,  200] loss :3.707 acc=4\n",
      "[21,  300] loss :3.706 acc=4\n",
      "[21,  400] loss :3.706 acc=3\n",
      "[21,  500] loss :3.707 acc=4\n",
      "For epoch 21 the test accuracy over the whole test set is 1 %\n",
      "[22,  100] loss :3.707 acc=0\n",
      "[22,  200] loss :3.705 acc=3\n",
      "[22,  300] loss :3.706 acc=4\n",
      "[22,  400] loss :3.705 acc=3\n",
      "[22,  500] loss :3.707 acc=1\n",
      "For epoch 22 the test accuracy over the whole test set is 1 %\n",
      "[23,  100] loss :3.705 acc=0\n",
      "[23,  200] loss :3.708 acc=0\n",
      "[23,  300] loss :3.706 acc=1\n",
      "[23,  400] loss :3.707 acc=3\n",
      "[23,  500] loss :3.705 acc=3\n",
      "For epoch 23 the test accuracy over the whole test set is 1 %\n",
      "[24,  100] loss :3.704 acc=4\n",
      "[24,  200] loss :3.704 acc=6\n",
      "[24,  300] loss :3.707 acc=3\n",
      "[24,  400] loss :3.707 acc=1\n",
      "[24,  500] loss :3.709 acc=6\n",
      "For epoch 24 the test accuracy over the whole test set is 1 %\n",
      "[25,  100] loss :3.705 acc=1\n",
      "[25,  200] loss :3.708 acc=1\n",
      "[25,  300] loss :3.705 acc=0\n",
      "[25,  400] loss :3.706 acc=4\n",
      "[25,  500] loss :3.707 acc=0\n",
      "For epoch 25 the test accuracy over the whole test set is 1 %\n",
      "[26,  100] loss :3.706 acc=0\n",
      "[26,  200] loss :3.705 acc=0\n",
      "[26,  300] loss :3.703 acc=1\n",
      "[26,  400] loss :3.707 acc=1\n",
      "[26,  500] loss :3.709 acc=1\n",
      "For epoch 26 the test accuracy over the whole test set is 1 %\n",
      "[27,  100] loss :3.706 acc=3\n",
      "[27,  200] loss :3.707 acc=3\n",
      "[27,  300] loss :3.705 acc=1\n",
      "[27,  400] loss :3.707 acc=6\n",
      "[27,  500] loss :3.707 acc=3\n",
      "For epoch 27 the test accuracy over the whole test set is 1 %\n",
      "[28,  100] loss :3.707 acc=3\n",
      "[28,  200] loss :3.707 acc=0\n",
      "[28,  300] loss :3.706 acc=6\n",
      "[28,  400] loss :3.703 acc=0\n",
      "[28,  500] loss :3.708 acc=4\n",
      "For epoch 28 the test accuracy over the whole test set is 1 %\n",
      "[29,  100] loss :3.706 acc=0\n",
      "[29,  200] loss :3.707 acc=6\n",
      "[29,  300] loss :3.705 acc=0\n",
      "[29,  400] loss :3.706 acc=0\n",
      "[29,  500] loss :3.708 acc=4\n",
      "For epoch 29 the test accuracy over the whole test set is 1 %\n",
      "[30,  100] loss :3.706 acc=3\n",
      "[30,  200] loss :3.707 acc=0\n",
      "[30,  300] loss :3.704 acc=3\n",
      "[30,  400] loss :3.707 acc=0\n",
      "[30,  500] loss :3.706 acc=3\n",
      "For epoch 30 the test accuracy over the whole test set is 1 %\n",
      "[31,  100] loss :3.706 acc=0\n",
      "[31,  200] loss :3.704 acc=1\n",
      "[31,  300] loss :3.705 acc=3\n",
      "[31,  400] loss :3.710 acc=1\n",
      "[31,  500] loss :3.706 acc=4\n",
      "For epoch 31 the test accuracy over the whole test set is 1 %\n",
      "[32,  100] loss :3.703 acc=4\n",
      "[32,  200] loss :3.707 acc=0\n",
      "[32,  300] loss :3.704 acc=3\n",
      "[32,  400] loss :3.712 acc=1\n",
      "[32,  500] loss :3.705 acc=3\n",
      "For epoch 32 the test accuracy over the whole test set is 1 %\n",
      "[33,  100] loss :3.710 acc=1\n",
      "[33,  200] loss :3.708 acc=0\n",
      "[33,  300] loss :3.705 acc=4\n",
      "[33,  400] loss :3.705 acc=4\n",
      "[33,  500] loss :3.704 acc=3\n",
      "For epoch 33 the test accuracy over the whole test set is 1 %\n",
      "[34,  100] loss :3.702 acc=4\n",
      "[34,  200] loss :3.707 acc=3\n",
      "[34,  300] loss :3.709 acc=1\n",
      "[34,  400] loss :3.706 acc=4\n",
      "[34,  500] loss :3.708 acc=3\n",
      "For epoch 34 the test accuracy over the whole test set is 1 %\n",
      "[35,  100] loss :3.708 acc=1\n",
      "[35,  200] loss :3.707 acc=1\n",
      "[35,  300] loss :3.706 acc=6\n",
      "[35,  400] loss :3.707 acc=1\n",
      "[35,  500] loss :3.705 acc=1\n",
      "For epoch 35 the test accuracy over the whole test set is 1 %\n",
      "[36,  100] loss :3.705 acc=0\n",
      "[36,  200] loss :3.707 acc=3\n",
      "[36,  300] loss :3.707 acc=3\n",
      "[36,  400] loss :3.707 acc=0\n",
      "[36,  500] loss :3.705 acc=0\n",
      "For epoch 36 the test accuracy over the whole test set is 1 %\n",
      "[37,  100] loss :3.706 acc=3\n",
      "[37,  200] loss :3.707 acc=1\n",
      "[37,  300] loss :3.706 acc=6\n",
      "[37,  400] loss :3.707 acc=1\n",
      "[37,  500] loss :3.705 acc=4\n",
      "For epoch 37 the test accuracy over the whole test set is 1 %\n",
      "[38,  100] loss :3.707 acc=1\n",
      "[38,  200] loss :3.706 acc=4\n",
      "[38,  300] loss :3.705 acc=1\n",
      "[38,  400] loss :3.707 acc=4\n",
      "[38,  500] loss :3.706 acc=0\n",
      "For epoch 38 the test accuracy over the whole test set is 1 %\n",
      "[39,  100] loss :3.709 acc=6\n",
      "[39,  200] loss :3.705 acc=1\n",
      "[39,  300] loss :3.702 acc=3\n",
      "[39,  400] loss :3.708 acc=1\n",
      "[39,  500] loss :3.708 acc=4\n",
      "For epoch 39 the test accuracy over the whole test set is 1 %\n",
      "[40,  100] loss :3.709 acc=3\n",
      "[40,  200] loss :3.704 acc=0\n",
      "[40,  300] loss :3.708 acc=0\n",
      "[40,  400] loss :3.703 acc=3\n",
      "[40,  500] loss :3.706 acc=1\n",
      "For epoch 40 the test accuracy over the whole test set is 1 %\n",
      "[41,  100] loss :3.706 acc=3\n",
      "[41,  200] loss :3.707 acc=0\n",
      "[41,  300] loss :3.706 acc=0\n",
      "[41,  400] loss :3.709 acc=1\n",
      "[41,  500] loss :3.705 acc=7\n",
      "For epoch 41 the test accuracy over the whole test set is 1 %\n",
      "[42,  100] loss :3.708 acc=0\n",
      "[42,  200] loss :3.704 acc=1\n",
      "[42,  300] loss :3.709 acc=0\n",
      "[42,  400] loss :3.706 acc=0\n",
      "[42,  500] loss :3.704 acc=4\n",
      "For epoch 42 the test accuracy over the whole test set is 1 %\n",
      "[43,  100] loss :3.708 acc=3\n",
      "[43,  200] loss :3.705 acc=1\n",
      "[43,  300] loss :3.706 acc=7\n",
      "[43,  400] loss :3.707 acc=0\n",
      "[43,  500] loss :3.705 acc=6\n",
      "For epoch 43 the test accuracy over the whole test set is 1 %\n",
      "[44,  100] loss :3.709 acc=3\n",
      "[44,  200] loss :3.705 acc=1\n",
      "[44,  300] loss :3.709 acc=1\n",
      "[44,  400] loss :3.706 acc=3\n",
      "[44,  500] loss :3.704 acc=3\n",
      "For epoch 44 the test accuracy over the whole test set is 1 %\n",
      "[45,  100] loss :3.705 acc=1\n",
      "[45,  200] loss :3.705 acc=4\n",
      "[45,  300] loss :3.707 acc=3\n",
      "[45,  400] loss :3.710 acc=0\n",
      "[45,  500] loss :3.703 acc=0\n",
      "For epoch 45 the test accuracy over the whole test set is 1 %\n",
      "[46,  100] loss :3.704 acc=1\n",
      "[46,  200] loss :3.706 acc=1\n",
      "[46,  300] loss :3.705 acc=4\n",
      "[46,  400] loss :3.710 acc=1\n",
      "[46,  500] loss :3.708 acc=3\n",
      "For epoch 46 the test accuracy over the whole test set is 1 %\n",
      "[47,  100] loss :3.704 acc=1\n",
      "[47,  200] loss :3.709 acc=1\n",
      "[47,  300] loss :3.705 acc=6\n",
      "[47,  400] loss :3.706 acc=3\n",
      "[47,  500] loss :3.707 acc=4\n",
      "For epoch 47 the test accuracy over the whole test set is 1 %\n",
      "[48,  100] loss :3.708 acc=0\n",
      "[48,  200] loss :3.704 acc=0\n",
      "[48,  300] loss :3.706 acc=3\n",
      "[48,  400] loss :3.706 acc=0\n",
      "[48,  500] loss :3.708 acc=1\n",
      "For epoch 48 the test accuracy over the whole test set is 1 %\n",
      "[49,  100] loss :3.706 acc=0\n",
      "[49,  200] loss :3.707 acc=0\n",
      "[49,  300] loss :3.704 acc=4\n",
      "[49,  400] loss :3.707 acc=1\n",
      "[49,  500] loss :3.708 acc=1\n",
      "For epoch 49 the test accuracy over the whole test set is 1 %\n",
      "[50,  100] loss :3.707 acc=1\n",
      "[50,  200] loss :3.708 acc=1\n",
      "[50,  300] loss :3.705 acc=4\n",
      "[50,  400] loss :3.705 acc=4\n",
      "[50,  500] loss :3.707 acc=3\n",
      "For epoch 50 the test accuracy over the whole test set is 1 %\n",
      "---finish---\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 数据升维\n",
    "data_train = np.expand_dims(data_train,axis=1)*1e3\n",
    "data_test = np.expand_dims(data_test,axis=1)*1e3\n",
    "label_train = label_train.flatten()\n",
    "label_test = label_test.flatten()\n",
    "np.swapaxes(data_train,1,2)\n",
    "np.swapaxes(data_test,1,2)\n",
    "print('data shape: ', data_train.shape, 'label shape: ', label_train.shape)\n",
    "# 生成data_loader\n",
    "print('get dataloader...', end='',flush=True)\n",
    "data_train_t = torch.tensor(data_train)\n",
    "label_train_t = torch.tensor(label_train)\n",
    "data_test_t = torch.tensor(data_test)\n",
    "label_test_t = torch.tensor(label_test)\n",
    "\n",
    "\n",
    "\n",
    "train_dataset = torch.utils.data.TensorDataset(data_train_t, label_train_t)\n",
    "test_dataset = torch.utils.data.TensorDataset(data_test_t, label_test_t)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    dataset=train_dataset,      # torch TensorDataset format\n",
    "    batch_size=64,      # mini batch size\n",
    "    shuffle=True,               # 要不要打乱数据 (打乱比较好)\n",
    "    num_workers=2,              # 多线程来读数据\n",
    "    drop_last = True,\n",
    "   \n",
    ")\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    dataset=test_dataset,      # torch TensorDataset format\n",
    "    batch_size=1000,      # mini batch size\n",
    "    shuffle=False,               # 要不要打乱数据 (打乱比较好)\n",
    "    num_workers=2,              # 多线程来读数据\n",
    "    drop_last = True,\n",
    ")\n",
    "print('[ok]')\n",
    "# 模型训练\n",
    "train(device, 50, train_loader, test_loader)\n",
    "\n",
    "\n",
    "print('---finish---') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bfea3b1-49ab-4729-b61a-c8891d701527",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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